Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics

Comparative analysis of the longissimus muscle proteome of European wild boar and domestic pig in response to thermal processing

This study tries to fill the knowledge gap regarding differences in the expression of proteins in the meat of European wild boar (Sus scrofa scrofa) and domestic pig (Sus scrofa domestica), considering the impact of thermally induced degradation. We assessed relative protein changes between cooked longissimus thoracis et lumborum (LTL) muscle proteomes by using mass spectrometry, chemometric, label-free proteomic, and bioinformatic tools. Among 30 differentially abundant proteins identified MyHC-2a, ATPs-α, CK-S, ADP/ATPt1, IDH2, and MyBP-C1 were upregulated (x > 1) whereas NEB, γ-ENO and EPSF were downregulated (x < 1) in wild boar. ShinyGO and KEGG database pathway analyses revealed that these proteins are mainly involved in processes related to muscle contraction and various pathways of glucose metabolism and energy production. Protein expression changes could have been caused by the different muscle activity of wild animals in response to prolonged movement associated with foraging for food in the natural environment.

LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems

The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Surfaceome analysis of extracellular vesicles from senescent cells uncovers uptake repressor DPP4

Senescent cells are beneficial for repairing acute tissue damage, but they are harmful when they accumulate in tissues, as occurs with advancing age. Senescence-associated extracellular vesicles (S-EVs) can mediate cell-to-cell communication and export intracellular content to the microenvironment of aging tissues. Here, we studied the uptake of EVs from senescent cells (S-EVs) and proliferating cells (P-EVs) and found that P-EVs were readily taken up by proliferating cells (fibroblasts and cervical cancer cells) while S-EVs were not. We thus investigated the surface proteome (surfaceome) of P-EVs relative to S-EVs derived from cells that had reached senescence via replicative exhaustion, exposure to ionizing radiation, or treatment with etoposide. We found that relative to P-EVs, S-EVs from all senescence models were enriched in proteins DPP4, ANXA1, ANXA6, S10AB, AT1A1, and EPHB2. Among them, DPP4 was found to selectively prevent uptake by proliferating cells, as ectopic overexpression of DPP4 in HeLa cells rendered DPP4-expressing EVs that were no longer taken up by other proliferating cells. We propose that DPP4 on the surface of S-EVs makes these EVs refractory to internalization by proliferating cells, advancing our knowledge of the impact of senescent cells in aging-associated processes.

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.

The effect of obesity on uterine receptivity is mediated by endometrial extracellular vesicles that control human endometrial stromal cell decidualization and trophoblast invasion

The objectives of the present study were to determine whether obesity impacts human decidualization and the endometrial control of trophoblast invasion (both of which are required for embryo implantation) and evaluate the potential involvement of endometrial extracellular vesicles (EVs) in the regulation of these physiological processes. Using primary human cell cultures, we first demonstrated that obesity is associated with significantly lower in vitro decidualization of endometrial stromal cells (ESCs). We then showed that a trophoblastic cell line's invasive ability was greater in the presence of conditioned media from cultures of ESCs from obese women. The results of functional assays indicated that supplementation of the culture medium with EVs from nonobese women can rescue (at least in part) the defect in in vitro decidualization described in ESCs from obese women. Furthermore, exposure to endometrial EVs from obese women (vs. nonobese women) was associated with significantly greater invasive activity by HTR-8/SVneo cells. Using mass-spectrometry-based quantitative proteomics, we found that EVs isolated from uterine supernatants of biopsies from obese women (vs. nonobese women) presented a molecular signature focused on cell remodelling and angiogenesis. The proteomics analysis revealed two differentially expressed proteins (fibronectin and angiotensin-converting enzyme) that might be involved specifically in the rescue of the decidualization capacity in ESCs from obese women; both of these proteins are abundantly present in endometrial EVs from nonobese women, and both are involved in the decidualization process. In conclusion, our results provided new insights into the endometrial EVs' pivotal role in the poor uterine receptivity observed in obese women.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

Challenges and Opportunities for Single-cell Computational Proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Termination factor Rho mediates transcriptional reprogramming of Bacillus subtilis stationary phase

Transcription termination factor Rho is known for its ubiquitous role in suppression of pervasive, mostly antisense, transcription. In the model Gram-positive bacterium Bacillus subtilis, de-repression of pervasive transcription by inactivation of rho revealed the role of Rho in the regulation of post-exponential differentiation programs. To identify other aspects of the regulatory role of Rho during adaptation to starvation, we have constructed a B. subtilis strain (Rho+) that expresses rho at a relatively stable high level in order to compensate for its decrease in the wild-type cells entering stationary phase. The RNAseq analysis of Rho+, WT and Δrho strains (expression profiles can be visualized at http://genoscapist.migale.inrae.fr/seb_rho/) shows that Rho over-production enhances the termination efficiency of Rho-sensitive terminators, thus reducing transcriptional read-through and antisense transcription genome-wide. Moreover, the Rho+ strain exhibits global alterations of sense transcription with the most significant changes observed for the AbrB, CodY, and stringent response regulons, forming the pathways governing the transition to stationary phase. Subsequent physiological analyses demonstrated that maintaining rho expression at a stable elevated level modifies stationary phase-specific physiology of B. subtilis cells, weakens stringent response, and thereby negatively affects the cellular adaptation to nutrient limitations and other stresses, and blocks the development of genetic competence and sporulation. These results highlight the Rho-specific termination of transcription as a novel element controlling stationary phase. The release of this control by decreasing Rho levels during the transition to stationary phase appears crucial for the functionality of complex gene networks ensuring B. subtilis survival in stationary phase.

Proteomic, mechanical, and biochemical development of tissue-engineered neocartilage

Background

The self-assembling process of cartilage tissue engineering is a promising technique to heal cartilage defects, preventing osteoarthritic changes. Given that chondrocytes dedifferentiate when expanded, it is not known if cellular expansion affects the development of self-assembled neocartilage. The objective of this study was to use proteomic, mechanical, and biochemical analyses to quantitatively investigate the development of self-assembled neocartilage derived from passaged, rejuvenated costal chondrocytes.

Methods

Yucatan minipig costal chondrocytes were used to create self-assembled neocartilage constructs. After 1, 4, 7, 14, 28, 56, or 84 days of self-assembly, constructs were analyzed through a variety of histological, biomechanical, biochemical, and proteomic techniques.

Results

It was found that temporal trends in neocartilage formation are similar to those seen in native hyaline articular cartilage development. For example, between days 7 and 84 of culture, tensile Young’s modulus increased 4.4-times, total collagen increased 2.7-times, DNA content decreased 69.3%, collagen type II increased 1.5-times, and aggrecan dropped 55.3%, mirroring trends shown in native knee cartilage. Importantly, collagen type X, which is associated with cartilage calcification, remained at low levels (≤ 0.05%) at all neocartilage developmental time points, similar to knee cartilage (< 0.01%) and unlike donor rib cartilage (0.98%).

Conclusions

In this work, bottom-up proteomics, a powerful tool to interrogate tissue composition, was used for the first time to quantify and compare the proteome of a developing engineered tissue to a recipient tissue. Furthermore, it was shown that self-assembled, costal chondrocyte-derived neocartilage is suitable for a non-homologous approach in the knee.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40824-022-00284-4.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

Urinary Proteomic Profile of Arterial Stiffness Is Associated With Mortality and Cardiovascular Outcomes

Background

The underlying mechanisms of arterial stiffness remain not fully understood. This study aimed to identify a urinary proteomic profile to illuminate its pathogenesis and to determine the prognostic value of the profile for adverse outcomes.

Methods and Results

We measured aortic stiffness using pulse wave velocity (PWV) and analyzed urinary proteome using capillary electrophoresis coupled with mass spectrometry in 669 randomly recruited Flemish patients (mean age, 50.2 years; 51.1% women). We developed a PWV‐derived urinary proteomic score (PWV‐UP) by modeling PWV with proteomics data at baseline through orthogonal projections to latent structures. PWV‐UP that consisted of 2336 peptides explained the 65% variance of PWV, higher than 36% explained by clinical risk factors. PWV‐UP was significantly associated with PWV (adjusted β=0.73 [95% CI, 0.67–0.79]; P<0.0001). Over 9.2 years (median), 36 participants died, and 75 experienced cardiovascular events. The adjusted hazard ratios (+1 SD) were 1.46 (95% CI, 1.08–1.97) for all‐cause mortality, 2.04 (95% CI, 1.07–3.87) for cardiovascular mortality, and 1.39 (95% CI, 1.11–1.74) for cardiovascular events (P≤0.031). For PWV, the corresponding estimates were 1.25 (95% CI, 0.97–1.60), 1.35 (95% CI, 0.85–2.15), and 1.22 (95% CI, 1.02–1.47), respectively (P≥0.033). Pathway analysis revealed that the peptides in PWV‐UP mostly involved multiple pathways, including collagen turnover, cell adhesion, inflammation, and lipid metabolism.

Conclusions

PWV‐UP was highly associated with PWV and could be used as a biomarker of arterial stiffness. PWV‐UP, but not PWV, was associated with all‐cause mortality and cardiovascular mortality, implying that PWV‐UP–associated peptides may be multifaceted and involved in diverse pathological processes beyond arterial stiffness.

Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum

Microbes that can recycle one-carbon (C₁) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO₂ and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H₂, or CO+CO₂+H₂). We detect the prioritization of proteome allocation for C₁ fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H₂ metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations.

IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C₁ waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Venom proteomic analysis of medically important Nigerian viper Echis ocellatus and Bitis arietans snake species

Snakebite envenoming remains a neglected tropical disease which poses severe health hazard, especially for the rural inhabitants in Africa. In Nigeria, vipers are responsible for the highest number of deaths. Hydrophilic interaction liquid chromatography coupled with LC-MS/MS was used to analyze the crude venoms of Echis ocellatus (Carpet viper) and Bitis arietans (Puff adder) in order to understand their venom proteomic identities. Results obtained revealed that gel-free proteomic analysis of the crude venoms led to the identification of 85 and 79 proteins, respectively. Seventy-eight (78) proteins were common between the two snake species with a 91.8% similarity score. The identified proteins belong to 18 protein families in E. ocellatus and 14 protein families in B. arietans. Serine proteases (22.31%) and metalloproteinases (21.06%) were the dominant proteins in the venom of B. arietans; while metalloproteinases (34.84%), phospholipase A₂s (21.19%) and serine proteases (15.50%) represent the major toxins in the E. ocellatus venom. Other protein families such as three-finger toxins and cysteine-rich venom proteins were detected in low proportions. This study provides an insight into the venom proteomic analysis of the two Nigerian viper species, which could be useful in identifying the toxin families to be neutralized in case of envenomation.

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Venom proteomic of Nigeria's most medically important snakes is presented.

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SVMP, SVSP and PLA₂ were the major toxin families in E. ocellatus and B. arietans.

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The venom proteomes of these vipers displayed 91.8% similarity in composition.

Advances in functional proteomics to study plant-pathogen interactions

Pathogen infection triggers complex signaling networks in plant cells that ultimately result in either susceptibility or resistance. We have made substantial progress in dissecting many of these signaling events, and it is becoming clear that changes in proteome composition and protein activity are major drivers of plant-microbe interactions. Here, we highlight different approaches to analyze the functional proteomes of hosts and pathogens and discuss how they have been used to further our understanding of plant disease. Global proteome profiling can quantify the dynamics of proteins, posttranslational modifications, and biological pathways that contribute to immune-related outcomes. In addition, emerging techniques such as enzyme activity-based profiling, proximity labeling, and kinase-substrate profiling are being used to dissect biochemical events that operate during infection. Finally, we discuss how these functional approaches can be integrated with other profiling data to gain a mechanistic, systems-level view of plant and pathogen signaling.

Evaluating Spatiotemporal Dynamics of Phosphorylation of RNA Polymerase II Carboxy-Terminal Domain by Ultraviolet Photodissociation Mass Spectrometry

The critical role of site-specific phosphorylation in eukaryotic transcription has motivated efforts to decipher the complex phosphorylation patterns exhibited by the carboxyl-terminal domain (CTD) of RNA polymerase II. Phosphorylation remains a challenging post-translational modification to characterize by mass spectrometry owing to the labile phosphate ester linkage and low stoichiometric prevalence, two features that complicate analysis by high-throughput MS/MS methods. Identifying phosphorylation sites represents one significant hurdle in decrypting the CTD phosphorylation, a problem exaggerated by a large number of potential phosphorylation sites. An even greater obstacle is decoding the dynamic phosphorylation pattern along the length of the periodic CTD sequence. Ultraviolet photodissociation (UVPD) is a high-energy ion activation method that provides ample backbone cleavages of peptides while preserving labile post-translational modifications that facilitate their confident localization. Herein, we report a quantitative parallel reaction monitoring (PRM) method developed to monitor spatiotemporal changes in site-specific Ser5 phosphorylation of the CTD by cyclin-dependent kinase 7 (CDK7) using UVPD for sequence identification, phosphosite localization, and differentiation of phosphopeptide isomers. We capitalize on the series of phospho-retaining fragment ions produced by UVPD to create unique transition lists that are pivotal for distinguishing the array of phosphopeptides generated from the CTD.

Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools

MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to data sets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and amounts. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-abundance ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies.

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Coelimycin Synthesis Activatory Proteins Are Key Regulators of Specialized Metabolism and Precursor Flux in Streptomyces coelicolor A3(2)

Many microbial specialized metabolites are industrially relevant agents but also serve as signaling molecules in intra-species and even inter-kingdom interactions. In the antibiotic-producing Streptomyces, members of the SARP (Streptomyces antibiotic regulatory proteins) family of regulators are often encoded within biosynthetic gene clusters and serve as their direct activators. Coelimycin is the earliest, colored specialized metabolite synthesized in the life cycle of the model organism Streptomyces coelicolor A3(2). Deletion of its two SARP activators cpkO and cpkN abolished coelimycin synthesis and resulted in dramatic changes in the production of the later, stationary-phase antibiotics. The underlying mechanisms of these phenotypes were deregulation of precursor flux and quorum sensing, as shown by label-free, bottom-up shotgun proteomics. Detailed profiling of promoter activities demonstrated that CpkO is the upper-level cluster activator that induces CpkN, while CpkN activates type II thioesterase ScoT, necessary for coelimycin synthesis. What is more, we show that cpkN is regulated by quorum sensing gamma-butyrolactone receptor ScbR.

Detecting differential protein abundance by combining peptide level P-values

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics

Data independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).

ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities

Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of 'Protein Presence,' an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/. SIGNIFICANCE: Integrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of 'Protein Presence' demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.

New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease

Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label-free (LF) differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis of cortical protein extract from zQ175 mouse brains, model of Huntington Disease (HD), and control animals (raw data available via ProteomeXchange with identifier PXD017471). LF data were also validated by western blot and MRM based experiments. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Overall, the CBN(P) method was demonstrated to be the most reliable and sensitive in detecting small differences in protein amounts when applied to biological samples.

Quantitative proteomic strategies to study reproduction in farm animals: Female reproductive fluids

Reproductive fluids from the female reproductive tract are gaining attention for their potential to support and optimize reproductive processes, including gamete maturation and embryo culture in vitro. Quantitative proteomics is a powerful way to decipher the proteome of reproductive tract fluids and to identify biologically relevant proteins. The present review describes proteomic strategies for analysing female reproductive fluid proteins. In addition, it considers the strategies for the preparation of oviductal, uterine and follicular fluid samples. Finally, it highlights the main results of quantitative proteomic studies, providing insights into the biological processes related to reproductive biology in farm animals. SIGNIFICANCE: Assisted reproductive technologies (ARTs) have become vitally important for farm animal breeding and much effort is going into the optimization and refinement of the techniques. There are also attempts to imitate physiological conditions by adding reproductive fluids or individual fluid proteins to improve in vitro procedures. A detailed knowledge of the reproductive fluid proteomes is indispensable. The present review summarizes the most widely used quantitative proteomic approaches for the analysis of fluids from the female reproductive tract and highlights the potential of quantitative proteomics to delineate reproductive processes and identify candidate proteins for ARTs in farm animals.

Expression of genes of the Pho regulon is altered in Streptomyces coelicolor

Most currently used antibiotics originate from Streptomycetes and phosphate limitation is an important trigger of their biosynthesis. Understanding the molecular processes underpinning such regulation is of crucial importance to exploit the great metabolic diversity of these bacteria and get a better understanding of the role of these molecules in the physiology of the producing bacteria. To contribute to this field, a comparative proteomic analysis of two closely related model strains, Streptomyces lividans and Streptomyces coelicolor was carried out. These strains possess identical biosynthetic pathways directing the synthesis of three well-characterized antibiotics (CDA, RED and ACT) but only S. coelicolor expresses them at a high level. Previous studies established that the antibiotic producer, S. coelicolor, is characterized by an oxidative metabolism and a reduced triacylglycerol content compared to the none producer, S. lividans, characterized by a glycolytic metabolism. Our proteomic data support these findings and reveal that these drastically different metabolic features could, at least in part, due to the weaker abundance of proteins of the two component system PhoR/PhoP in S. coelicolor compared to S. lividans. In condition of phosphate limitation, PhoR/PhoP is known to control positively and negatively, respectively, phosphate and nitrogen assimilation and our study revealed that it might also control the expression of some genes of central carbon metabolism. The tuning down of the regulatory role of PhoR/PhoP in S. coelicolor is thus expected to be correlated with low and high phosphate and nitrogen availability, respectively and with changes in central carbon metabolic features. These changes are likely to be responsible for the observed differences between S. coelicolor and S. lividans concerning energetic metabolism, triacylglycerol biosynthesis and antibiotic production. Furthermore, a novel view of the contribution of the bio-active molecules produced in this context, to the regulation of the energetic metabolism of the producing bacteria, is proposed and discussed.

MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics

Missing values are a major issue in quantitative data-dependent mass spectrometry-based proteomics. We therefore present an innovative solution to this key issue by introducing a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based model component. It enables dramatically enhanced quantification of proteins with many missing values without having to resort to harmful assumptions for missingness. We demonstrate the superior performance of our method by comparing it with state-of-the-art methods in the field.

Review of Three-Dimensional Liquid Chromatography Platforms for Bottom-Up Proteomics

Proteomics is a large-scale study of proteins, aiming at the description and characterization of all expressed proteins in biological systems. The expressed proteins are typically highly complex and large in abundance range. To fulfill high accuracy and sensitivity of proteome analysis, the hybrid platforms of multidimensional (MD) separations and mass spectrometry have provided the most powerful solution. Multidimensional separations provide enhanced peak capacity and reduce sample complexity, which enables mass spectrometry to analyze more proteins with high sensitivity. Although two-dimensional (2D) separations have been widely used since the early period of proteomics, three-dimensional (3D) separation was barely used by low reproducibility of separation, increased analysis time in mass spectrometry. With developments of novel microscale techniques such as nano-UPLC and improvements of mass spectrometry, the 3D separation becomes a reliable and practical selection. This review summarizes existing offline and online 3D-LC platforms developed for proteomics and their applications. In detail, setups and implementation of those systems as well as their advances are outlined. The performance of those platforms is also discussed and compared with the state-of-the-art 2D-LC. In addition, we provide some perspectives on the future developments and applications of 3D-LC in proteomics.

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Exploring the impact of Mg-doped ZnO nanoparticles on a model soil microorganism Bacillus subtilis

The environmental contamination of soil by metal oxide nanomaterials is a growing global concern because of their potential toxicity. We investigated the effects of Mg doped ZnO (Mg-nZnO) nanoparticles on a model soil microorganism Bacillus subtilis. Mg-nZnO exhibited only a moderate toxic effect on B. subtilis vegetative cells but was able to prevent biofilm formation and destroy already formed biofilms. Similarly, Mg-nZnO (≤1 mg/mL) was moderately toxic towards Listeria monocytogenes, Staphylococcus aureus, Escherichia coli, Salmonella enterica, Saccharomyces cerevisiae and murine macrophages. Engineered Mg-nZnO produced H₂O₂ and O₂^•- radicals in solutions of various salt and organic molecule compositions. A quantitative proteomic analysis of B. subtilis membrane proteins showed that Mg-nZnO increased the expression of proteins involved in detoxification of ROS, translation and biofilm formation. Overall, our results suggest that Mg-nZnO released into the environment may hinder the spreading, colonization and biofilm formation by B. subtilis but also induce a mechanism of bacterial adaptation.

Innovating the Concept and Practice of Two-Dimensional Gel Electrophoresis in the Analysis of Proteomes at the Proteoform Level

Two-dimensional gel electrophoresis (2DE) is an important and well-established technical platform enabling extensive top-down proteomic analysis. However, the long-held but now largely outdated conventional concepts of 2DE have clearly impacted its application to in-depth investigations of proteomes at the level of protein species/proteoforms. It is time to popularize a new concept of 2DE for proteomics. With the development and enrichment of the proteome concept, any given “protein” is now recognized to consist of a series of proteoforms. Thus, it is the proteoform, rather than the canonical protein, that is the basic unit of a proteome, and each proteoform has a specific isoelectric point (pI) and relative mass (M_r). Accordingly, using 2DE, each proteoform can routinely be resolved and arrayed according to its different pI and M_r. Each detectable spot contains multiple proteoforms derived from the same gene, as well as from different genes. Proteoforms derived from the same gene are distributed into different spots in a 2DE pattern. High-resolution 2DE is thus actually an initial level of separation to address proteome complexity and is effectively a pre-fractionation method prior to analysis using mass spectrometry (MS). Furthermore, stable isotope-labeled 2DE coupled with high-sensitivity liquid chromatography-tandem MS (LC-MS/MS) has tremendous potential for the large-scale detection, identification, and quantification of the proteoforms that constitute proteomes.

Interactome of the Autoimmune Risk Protein ANKRD55

The ankyrin repeat domain-55 (ANKRD55) gene contains intronic single nucleotide polymorphisms (SNPs) associated with risk to contract multiple sclerosis, rheumatoid arthritis or other autoimmune disorders. Risk alleles of these SNPs are associated with higher levels of ANKRD55 in CD4⁺ T cells. The biological function of ANKRD55 is unknown, but given that ankyrin repeat domains constitute one of the most common protein-protein interaction platforms in nature, it is likely to function in complex with other proteins. Thus, identification of its protein interactomes may provide clues. We identified ANKRD55 interactomes via recombinant overexpression in HEK293 or HeLa cells and mass spectrometry. One hundred forty-eight specifically interacting proteins were found in total protein extracts and 22 in extracts of sucrose gradient-purified nuclei. Bioinformatic analysis suggested that the ANKRD55-protein partners from total protein extracts were related to nucleotide and ATP binding, enriched in nuclear transport terms and associated with cell cycle and RNA, lipid and amino acid metabolism. The enrichment analysis of the ANKRD55-protein partners from nuclear extracts is related to sumoylation, RNA binding, processes associated with cell cycle, RNA transport, nucleotide and ATP binding. The interaction between overexpressed ANKRD55 isoform 001 and endogenous RPS3, the cohesins SMC1A and SMC3, CLTC, PRKDC, VIM, β-tubulin isoforms, and 14-3-3 isoforms were validated by western blot, reverse immunoprecipitaton and/or confocal microscopy. We also identified three phosphorylation sites in ANKRD55, with S436 exhibiting the highest score as likely 14-3-3 binding phosphosite. Our study suggests that ANKRD55 may exert function(s) in the formation or architecture of multiple protein complexes, and is regulated by (de)phosphorylation reactions. Based on interactome and subcellular localization analysis, ANKRD55 is likely transported into the nucleus by the classical nuclear import pathway and is involved in mitosis, probably via effects associated with mitotic spindle dynamics.

Simultaneous Quantification of Protein Expression and Modifications by Top-down Targeted Proteomics: A Case of the Sarcomeric Subproteome

Determining changes in protein expression and post-translational modifications (PTMs) is crucial for elucidating cellular signal transduction and disease mechanisms. Conventional antibody-based approaches have inherent problems such as the limited availability of high-quality antibodies and batch-to-batch variation. Top-down mass spectrometry (MS)-based proteomics has emerged as the most powerful method for characterization and quantification of protein modifications. Nevertheless, robust methods to simultaneously determine changes in protein expression and PTMs remain lacking. Herein, we have developed a straightforward and robust top-down liquid chromatography (LC)/MS-based targeted proteomics platform for simultaneous quantification of protein expression and PTMs with high throughput and high reproducibility. We employed this method to analyze the sarcomeric subproteome from various muscle types of different species, which successfully revealed skeletal muscle heterogeneity and cardiac developmental changes in sarcomeric protein isoform expression and PTMs. As demonstrated, this targeted top-down proteomics platform offers an excellent 'antibody-independent' alternative for the accurate quantification of sarcomeric protein expression and PTMs concurrently in complex mixtures, which is generally applicable to different species and various tissue types.

EGL-3 and EGL-21 are required to trigger nocifensive response of Caenorhabditis elegans to noxious heat

Caenorhabditis elegans (C. elegans) is a widely used model organism to examine nocifensive response to noxious stimuli, including heat avoidance. Recently, comprehensive analysis of the genome sequence revealed several pro-neuropeptide genes, encoding a series of bioactive neuropeptides. C. elegans neuropeptides are involved in the modulation of essentially all behaviors including locomotion, mechanosensation, thermosensation and chemosensation. The maturation of pro-neuropeptide to neuropeptide is performed by ortholog pro-protein convertases and carboxypeptidase E (e.g. EGL-3 and EGL-21). We hypothesized that C. elegans egl-3 or egl-21 mutants will have a significant decrease in mature neuropeptides and they will display an impaired heat avoidance behavior. Our data has shown that thermal avoidance behavior of egl-3 and egl-21 mutants was significantly hampered compared to WT(N2) C. elegans. Moreover, flp-18, flp-21 and npr-1 mutant C. elegans displayed a similar phenotype. EGL-3 pro-protein convertase and EGL-21 carboxypeptidase E are essential enzymes for the maturation of pro-neuropeptides to active neuropeptides in C. elegans. Quantitative mass spectrometry analyses with egl-3 and egl-21 mutant C. elegans homogenates demonstrated that proteolysis of ProFLP-18 and ProFLP-21 are severely impeded, leading to a lack of mature bioactive neuropeptides. Not only FLP-21 but also FLP-18 related mature neuropeptides, both are ligands of NPR-1 and are needed to trigger nocifensive response of C. elegans to noxious heat.

Mass Spectrometry-Based Biomarkers in Drug Development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Multiparameter Optimization of Two Common Proteomics Quantification Methods for Quantifying Low-Abundance Proteins

Quantitative proteomics has been extensively applied in the screening of differentially regulated proteins in various research areas for decades, but its sensitivity and accuracy have been a bottleneck for many applications. Every step in the proteomics workflow can potentially affect the quantification of low-abundance proteins, but a systematic evaluation of their effects has not been done yet. In this work, to improve the sensitivity and accuracy of label-free quantification and tandem mass tags (TMT) labeling in quantifying low-abundance proteins, multiparameter optimization was carried out using a complex 2-proteome artificial sample mixture for a series of steps from sample preparation to data analysis, including the desalting of peptides, peptide injection amount for LC-MS/MS, MS1 resolution, the length of LC-MS/MS gradient, AGC targets, ion accumulation time, MS2 resolution, precursor coisolation threshold, data analysis software, statistical calculation methods, and protein fold changes, and the best settings for each parameter were defined. The suitable cutoffs for detecting low-abundance proteins with at least 1.5-fold and 2-fold changes were identified for label-free and TMT methods, respectively. The use of optimized parameters will significantly improve the overall performance of quantitative proteomics in quantifying low-abundance proteins and thus promote its application in other research areas.

Peptide filtering differently affects the performances of XIC-based quantification methods

In bottom-up proteomics, data are acquired on peptides resulting from proteolysis. In XIC-based quantification, the quality of the estimation of protein abundance depends on how peptide data are filtered and on which quantification method is used to express peptide intensity as protein abundance. So far, these two questions have been addressed independently. Here, we studied to what extent the relative performances of the quantification methods depend on the filters applied to peptide intensity data. To this end, we performed a spike-in experiment using Universal Protein Standard to evaluate the performances of five quantification methods in five datasets obtained after application of four peptide filters. Estimated protein abundances were not equally affected by filters depending on the computation mode and the type of data for quantification. Furthermore, we found that filters could have contrasting effects depending on the quantification objective. Intensity modeling proved to be the most robust method, providing the best results in the absence of any filter. However, the different quantification methods can achieve similar performances when appropriate peptide filters are used. Altogether, our findings provide insights into how best to handle intensity data according to the quantification objective and the experimental design. SIGNIFICANCE: We believe that our results are of major importance because they address, as far as we know for the first time, the crossed-effects of peptide intensity data filtering and XIC-based quantification methods on protein quantification. While previous papers have dealt with peptide filtering independently of the quantification method, here we combined four peptide filters (based on peptide sharing between proteins, retention time variability, peptides occurrence and peptide intensity profiles) with five XIC-based quantification methods representing different modes of calculating protein abundances from peptide intensities. For these different combinations, we analyzed the quality of protein quantification in terms of precision, accuracy and linearity of response to increasing protein concentration using a spike-in experiment. We showed that not only filters effect on the estimation of protein abundances depend on the quantification methods but also that quantification methods can reach similar performances when appropriate peptide filters are used. Also, depending on the quantification objective, i.e. absolute or relative, filters can have contrasting effects and we demonstrated that protein quantification by the peptide intensity modeling was the most robust method.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline

Label-free quantification has grown in popularity as a means of obtaining relative abundance measures for proteomics experiments. However, easily accessible and integrated tools to perform label-free quantification have been lacking. We describe StPeter, an implementation of Normalized Spectral Index quantification for wide availability through integration into the widely used Trans-Proteomic Pipeline. This implementation has been specifically designed for reproducibility and ease of use. We demonstrate that StPeter outperforms other state-of-the art packages using a recently reported benchmark data set over the range of false discovery rates relevant to shotgun proteomics results. We also demonstrate that the software is computationally efficient and supports data from a variety of instrument platforms and experimental designs. Results can be viewed within the Trans-Proteomic Pipeline graphical user interfaces and exported in standard formats for downstream statistical analysis. By integrating StPeter into the freely available Trans-Proteomic Pipeline, users can now obtain high-quality label-free quantification of any data set in seconds by adding a single command to the workflow.

Maillard Proteomics: Opening New Pages

Protein glycation is a ubiquitous non-enzymatic post-translational modification, formed by reaction of protein amino and guanidino groups with carbonyl compounds, presumably reducing sugars and α-dicarbonyls. Resulting advanced glycation end products (AGEs) represent a highly heterogeneous group of compounds, deleterious in mammals due to their pro-inflammatory effect, and impact in pathogenesis of diabetes mellitus, Alzheimer's disease and ageing. The body of information on the mechanisms and pathways of AGE formation, acquired during the last decades, clearly indicates a certain site-specificity of glycation. It makes characterization of individual glycation sites a critical pre-requisite for understanding in vivo mechanisms of AGE formation and developing adequate nutritional and therapeutic approaches to reduce it in humans. In this context, proteomics is the methodology of choice to address site-specific molecular changes related to protein glycation. Therefore, here we summarize the methods of Maillard proteomics, specifically focusing on the techniques providing comprehensive structural and quantitative characterization of glycated proteome. Further, we address the novel break-through areas, recently established in the field of Maillard research, i.e., in vitro models based on synthetic peptides, site-based diagnostics of metabolism-related diseases (e.g., diabetes mellitus), proteomics of anti-glycative defense, and dynamics of plant glycated proteome during ageing and response to environmental stress.

Proteomic characterisation of bovine and avian purified protein derivatives and identification of specific antigens for serodiagnosis of bovine tuberculosis

Background

Bovine purified protein derivative (bPPD) and avian purified protein derivative (aPPD) are widely used for bovine tuberculosis diagnosis. However, little is known about their qualitative and quantitative characteristics, which makes their standardisation difficult. In addition, bPPD can give false-positive tuberculosis results because of sequence homology between Mycobacterium bovis (M. bovis) and M. avium proteins. Thus, the objective of this study was to carry out a proteomic characterisation of bPPD, aPPD and an immunopurified subcomplex from bPPD called P22 in order to identify proteins contributing to cross-reactivity among these three products in tuberculosis diagnosis.

Methods

Trypsin digests of bPPD, aPPD and P22 were analysed by nanoscale liquid chromatography-electrospray ionization tandem mass spectrometry. Mice were immunised with bPPD or aPPD, and their serum was tested by indirect ELISA for reactivity against these preparations as well as against P22.

Results

A total of 456 proteins were identified in bPPD, 1019 in aPPD and 118 in P22; 146 of these proteins were shared by bPPD and aPPD, and 43 were present in all three preparations. Candidate proteins that may cause cross-reactivity between bPPD and aPPD were identified based on protein abundance and antigenic propensity. Serum reactivity experiments indicated that P22 may provide greater specificity than bPPD with similar sensitivity for ELISA-type detection of antibodies against M. tuberculosis complex.

Conclusion

The subpreparation from bPPD called P22 may be an alternative to bPPD for serodiagnosis of bovine tuberculosis, since it shares fewer proteins with aPPD than bPPD does, reducing risk of cross-reactivity with anti-M. avium antibodies.

Electronic supplementary material

The online version of this article (10.1186/s12014-017-9171-z) contains supplementary material, which is available to authorized users.

The ciliary membrane-associated proteome reveals actin-binding proteins as key components of cilia

Primary cilia are sensory, antennae-like organelles present on the surface of many cell types. They have been involved in a variety of diseases collectively termed ciliopathies. As cilia are essential regulators of cell signaling, the composition of the ciliary membrane needs to be strictly regulated. To understand regulatory processes at the ciliary membrane, we report the targeting of a genetically engineered enzyme specifically to the ciliary membrane to allow biotinylation and identification of the membrane-associated proteome. Bioinformatic analysis of the comprehensive dataset reveals high-stoichiometric presence of actin-binding proteins inside the cilium. Immunofluorescence stainings and complementary interaction proteomic analyses confirm these findings. Depolymerization of branched F-actin causes further enrichment of the actin-binding and actin-related proteins in cilia, including Myosin 5a (Myo5a). Interestingly, Myo5a knockout decreases ciliation while enhanced levels of Myo5a are observed in cilia upon induction of ciliary disassembly. In summary, we present a novel approach to investigate dynamics of the ciliary membrane proteome in mammalian cells and identify actin-binding proteins as mechanosensitive components of cilia that might have important functions in cilia membrane dynamics.

Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob).

SIGNIFICANCE

The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.